Configurable Puppeteer MCP Server

The MCP Configurable Puppeteer server brings the power of headless browser automation directly into AI‑driven workflows. By exposing a suite of tools that mirror common Puppeteer actions—navigation, clicking, form filling, screenshotting, and JavaScript evaluation—it lets language models interact with live web pages as if they were a human user. This capability is essential for tasks that require real‑time data extraction, dynamic content rendering, or automated testing of web interfaces.

What sets this server apart is its configurability. Developers can tweak Puppeteer’s launch options via a simple JSON string in the environment variable, enabling use of alternative browsers (e.g., Firefox), custom viewport sizes, or any other launch parameter without touching the server code. This flexibility allows teams to align browser behavior with production environments or compliance requirements, ensuring that AI interactions are consistent with real user experiences.

Key features include:

• Full browser automation: Navigate to any URL, interact with page elements, and submit forms.
• Visual feedback: Capture full‑page or element‑specific screenshots with adjustable dimensions, then retrieve them as resources.
• Debugging support: Stream console logs from the browser back to the AI client, aiding in troubleshooting page scripts or rendering issues.
• JavaScript execution: Run arbitrary scripts within the page context, enabling complex data extraction or state manipulation.

Typical use cases span automated web scraping where dynamic content must be rendered, end‑to‑end testing of single‑page applications, or data collection for training AI models that need up‑to‑date web information. In an MCP workflow, a Claude assistant can request the tool to open a site, then use to pull specific DOM values, and finally return a screenshot via the resource. The console log resource provides a transparent view of any errors or warnings that occurred during the session.

Because the server exposes its capabilities through the MCP schema, integrating it into existing AI pipelines is straightforward. Developers can compose tool calls in prompts, let the assistant decide which actions to perform, and retrieve resources directly—all without writing custom integration code. This combination of ease of use, configurability, and rich browser interaction makes the MCP Configurable Puppeteer a standout addition for developers seeking to bridge AI assistants with real‑world web environments.